Apparatus and method for creating effects in video

ABSTRACT

Creating a blur in a digital image by receiving original pixel values in a frame of image data, and for each pixel position in the digital image being blurred, calculating a blurred pixel value by applying a box filter having a plurality of elements with unity multiplier values to original pixel values for pixel positions that include the pixel position and pixel positions that are nearby to it. The output of the first box filter can be sequentially applied as inputs to a second box filter.

TECHNICAL FIELD

The invention relates to modifying video.

BACKGROUND

When a video program is created at a nonlinear video editing and compositing system, analog video source materials are typically digitized, compressed and stored on disc for later random access for use in creating a video program. Digital source video can also be input into the nonlinear video editor. Segments of the video, also referred to as “video clips,” can be taken from different sources, and the beginning and end points can be trimmed as desired during editing. The order of clips is established in a time line to determine the sequence, and the position of a clip can be changed. Clips can be deleted or inserted as desired. During the editing process, the clips of compressed video can be randomly accessed, decompressed and viewed in real time. The source video can be modified by various effects such as scale, rotate, blur using hardware or software filters to manipulate pixels on the fly in real time or offline.

A blur can be created by replacing the value for a pixel with a new value based upon the original values for that pixel and nearby pixels, with the values of the closer pixels being weighted more than the value of pixels that are further away. The new pixel value P_(blur) is a convolution, i.e., a sum of weighted values, given by the following formula:

P_(blur)=ΣP_(i)*W_(i)

-   -   Where: P_(i) equals the value of pixel i, which is a neighbor of         the pixel being calculated or the pixel itself, and     -   W_(i) equals the weight for pixel i.

If the weights are calculated using a Gaussian distribution, a blur will simulate a defocused image and be visually pleasing. The Gaussian weights W_(gi) can be calculated using the following formula:

W_(gi)e^(−x**2/c**2),

Where: x is the distance from pixel i to the pixel being calculated, and c is a constant.

Calculation of each blurred pixel value requires n multiply operations and n add operations, where n is the number of pixels used in the blur. E.g., if the blur were based on the values of pixels in 101 pixels in the same row and 101 pixels in the same column, there would be 202 multiplications and 202 additions to obtain a single blurred pixel value. This process would then need to be repeated for each pixel being blurred in each frame of the video program being subjected to the blur.

SUMMARY

In one aspect, the invention features, in general, creating a blur in a digital image by receiving original pixel values in a frame of image data, and for each pixel position in the digital image being blurred, calculating a blurred pixel value by applying a box filter having a plurality of elements with unity multiplier values to original pixel values for pixel positions that include the pixel position and pixel positions that are nearby to it.

In another aspect, the invention features, in general, creating a blur in a digital image by calculating, for each pixel position in the digital image being blurred, a blurred pixel value by sequentially applying the original pixel values as inputs to a first box filter that has a plurality of elements and by sequentially applying the outputs of the first box filter as the inputs to a second box filter.

Particular embodiments of the invention may include one or more of the following features. The outputs of the second box filter can be sequentially applied as the inputs to a third box filter. Additional box filters can be added if desired. The elements of the box filters can have unity multiplier values. Each box filter can be implemented by a storage area that maintains a current filter output and an adder and subtracter to update the filter output each time that a new input is received by adding the new input to the current filter output and subtracting the input that had been received m pixel positions earlier, where m is the number of elements in the box filter.

Embodiments of the invention may have one or more of the following advantages. Gaussian type blurs can be simply and quickly created with a small number of operations and/or a small number of hardware components by employing multiple box filters in series.

Other aspects and features of the invention will be apparent from the detailed description below and from the claims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a box diagram of a video editing and compositing system.

FIG. 2 is a diagram of a frame of pixels.

FIG. 3 is a diagram of a blur effects subsystem of the FIG. 1 system.

FIG. 4 is a representation illustrating the operation of a five-element box filter that has unity (1.0) multiplier values.

FIG. 5 is a representation illustrating application of the FIG. 4 box filter to a row of pixel data.

FIG. 6 is a diagram of components of the FIG. 4 box filter.

FIG. 7 is a representation of the five-component sequence that results from passing a single pixel value of 1.0 through the five-element box filter of FIG. 4.

FIG. 8 is a representation of the nine-component sequence that results from passing the five-component output of FIG. 6 through a five-element box filter that has unity (1.0) multiplier values.

FIG. 9 is a representation of the seventeen-component sequence that results from passing the nine-component output of FIG. 7 through a nine-element box filter that has unity (1.0) multiplier values.

FIG. 10 is a graph illustrating the weighting functions resulting from use of different numbers of box filters in sequence.

FIG. 11 is an alternative embodiment of a blur effects subsystem used to create a blur effect on image data.

DETAILED DESCRIPTION

Referring to FIG. 1, nonlinear video editing and compositing system 10 includes host computer 12 having monitor 14, user input devices 16 (e.g., keyboard and mouse), video editing and compositing software 18, video editing peripheral board 20 in an expansion slot of computer 12, plug-in video hardware filters 22 in expansion slots of computer 12 or otherwise connected thereto, video tape recorder (VTR) 24, and mass storage disc 26.

FIG. 2 shows a five-by-five block 100 of pixels 102 in a frame 104 of image data processed in video editing and compositing system 10. Central pixel 102H is located in column 106 and row 108. In creating a blur effect for the frame of image data, the value for each pixel being blurred is replaced by a new value based upon that pixel's original value and the value of nearby pixels in the same column and same row. System 10 employs sequential box filter operations to create modified pixel values with a reduced number of mathematical operations as compared to the convolution procedure described earlier.

Referring to FIG. 3, video effects subsystem 101 of video editing and compositing system 10 includes first and second frame buffers 110, 112, two five-element box filters 114, 116, a nine-element box filter 118, and a divider 119. Video effects subsystem 101 can be implemented in a plug-in hardware filter 22, in other hardware, or in software 18 (using random access memory in computer 12 for the frame buffers).

Referring to FIG. 4, five-element box filter 114 has five elements 120, each having unity (1.0) multiplier values. Filter 114 receives pixel values P in sequence, and the values are passed from one element 120 to the next. Each element 120 provides an intermediate result 122, which is the product of 1.0 times the pixel value presented to that element 120 at a particular time. The output 124 of box filter 114 at each time is the sum of intermediate results 122 at that time. FIG. 4 shows the pixel values P_(n−2), P_(n−1), P_(n), P_(n+1), P_(n+2) as the intermediate results for the case where the value for pixel n, Pn, is in the central position. Output 124 for pixel n is ΣP_(n), which is given by the following formula:

ΣP _(n) =P _(n−2) +P _(n−1) +P _(n) +P _(n+1) +P _(n+2).

Referring to FIG. 5, the application of box filter 114 to a portion of row 108 of pixel values is shown. The pixel values include:

P_(n−3)P_(n−2)P_(n−1)P_(n)P_(n+1)P_(n+2)P_(n+3)P_(n+4)

Bracket 130 shows the five pixel values present in elements 120 of box filter 114 (which are also the intermediate values 122, owing to the unity multiplier values for each element) for the case when pixel n is in the central position so as to provide and output of ΣP_(n). Bracket 132 shows the five pixel values present in elements 120 of box filter 114 (which are also the intermediate values 122) for the case when pixel n+1s in the central position so as to provide an output of ΣP_(n+1). ΣP_(n) is given by the formula set forth above. ΣP_(n+1) is given by the following formula:

ΣP _(n+1) =P _(n−1) +P _(n) +P _(n+1) +P _(n+2) +P _(n+3).

It is seen that the filter output for pixel n+1 is related to the output for pixel n in that P_(n−2) has been dropped from the left and P_(n+3 is) has been added at the right. ΣP_(n+1) can thus be derived from ΣP_(n) using the following formula:

ΣP _(n+1) =ΣP _(n) +P _(n+3) −P _(n−2)

An adder, a subtracter, and a storage element to store the current value of ΣP can thus implement box filter 114. Referring to FIG. 6, box filter 114 is shown implemented by adder 134, subtracter 135, and register 136. Temporary storage 137 (the same size as the number of elements 122 in the box filter times the bits in each pixel value) is used to provide the values being subtracted without the need to access the frame buffer 110, which is a less preferred alternative. Logic 138 is used to handle the situation at the beginning and end of lines before the elements 122 of the box filter have been filled up. Logic 138 can either provide constants (e.g., 0 or black) or mirror the values inside the image at the edge. After this priming to get the initial values for ΣP at the beginning of a line, each new ΣP can be determined by adding the new value at the right at adder 134 and subtracting the left-most value, which, for a box filter having m elements, is the value that had been received m pixel positions earlier, at subtracter 135. Box filters of additional elements, e.g., nine-element filter 118 (or even much larger filters with, e.g., 101 elements) can also be implemented with the same components. The box filter shown in FIG. 6 can be implemented in hardware or software, and result in a video effects subsystem that is more efficient in transistors (for hardware) and time (for software) than prior approaches .

Box filter 114 can be applied to the original pixel data in the rows in frame buffer 110, processing one row at time and placing the modified pixel values in the corresponding positions in frame buffer 112 to blur the pixel values in a horizontal manner. Box filter 114 can then be applied to the modified pixel values in frame buffer 112, processing one column at a time and placing the modified pixel values in the corresponding positions in frame buffer 110 to create a blur effect with both horizontal and vertical blurring. The values could be divided by 5 (implemented as a multiply times the inverse) before storage in each frame buffer, or by 10 before storage in one of the frame buffers, to provide scaling. (The values could also be scaled upon final removal from or prior to original storage in a frame buffer.) While such an effect can be implemented with a small number of components and a small number of operations, equal weight is given to all twenty-five original values in the 5×5 box in which the pixel is centered. Such equal weight blurring is generally not as desirable as blurring that gives a higher weight to closer pixel positions.

FIGS. 7-9 illustrate how box filters can be used in series in order to provide gradually increased weighting for closer pixel positions. For purposes of the illustration, assume that there is a single pixel value of 1.0 at one pixel position n in frame buffer 110, and zero values for all other pixel positions in a sequence (i.e., row or column) of original pixel data. FIG. 7 shows the sequence that results from passing the single pixel value of 1.0 through the five-element box filter 114 of FIGS. 3 and 4. It is seen that this results in values of 1.0 being output five times. If these output values are stored in a frame buffer 112, the 1.0 value of the pixel at position n is spread out to two neighbors on each side. In implementation, this would result in a constant weighting, as shown by curve 200 on the weighting graph shown in FIG. 10.

FIG. 8 shows what happens when the result of FIG. 6 (values of 1.0 at five adjacent positions, and zero for all other pixel positions) is then passed through a second five-element box filter (e.g., 116) having unity (1.0) multiplier values. The filter output 124 ΣP) for the pixel located at two pixel positions before the five pixel positions that include pixel n at the center will be based upon a single 1.0 value in the right most element 120, and thus have the value 1.0. The next pixel position will have two elements 120 with 1.0 in each one, such that the sum is 2.0. The next pixel position will have three elements 120 with 1.0 and a sum of 3.0. The next will have four and a sum of 4.0, and the next, the central pixel n, will have five elements 120 with a 1.0, and a sum of 5.0. As the pixel value sequence (1.0, 1.0, 1.0, 1.0, 1.0) continues through the box filter, the next will have four, and the next three and so on so that the sequence shown in FIG. 8 results. These further modified pixel values could be stored in frame buffer 110, replacing the original values at the corresponding positions. This shows weighting with pixel n getting the most weight, and the positions next to it getting gradually less weight in a linear manner, as illustrated by curve 202 OD the weighting graph shown in FIG. 10.

FIG. 9 shows what happens when the result of FIG. 8 (values of 1.0, 2.0, 3.0, 4.0, 5.0. 4.0, 3.0, 2.0, 1.0 at nine adjacent positions, with n at the center, and with zero values for all other pixel positions) is then passed through a nine-element box filter having unity (1.0) multiplier values. The filter output 124 (UP) for the pixel located at four pixel positions before the nine pixel positions that include pixel n at the center will be based upon a single 1.0 value in the right-most element 120, and thus have the value 1.0. The next pixel position will have one element 120 with 1.0 and another with 2.0, such that the sum is 3.0. The next pixel position will have three elements 120 with 1.0, 2.0 and 3.0 and a sum of 6.0, and so on as shown in FIG. 8. These further modified pixel values could be stored in frame buffer 112, replacing the values at the corresponding positions. This shows weighting with pixel n getting the most weight, and the positions next to it getting gradually less weight in very close to a Gaussian distribution, as illustrated by curve 204 on the weighting graph shown in FIG. 10.

FIGS. 7-9 demonstrate how a single pixel value of 1.0 at one position gets distributed to seventeen pixels (eight on each side) after passing through two five-element box filters 114, 116 and one nine-element box filter 118, all having elements 120 with 1.0 (unity) multipliers. If an actual pixel value were used as the single input instead of 1.0, it would have been effectively multiplied times the numbers shown in FIG. 9 owing to the additions that result in the sequential passage of inputs through the elements of the box filters. If actual non-zero pixel values were located at all pixel positions in the row (or column), they would similarly be distributed over nearby pixels with the same weighting. Thus the values at P_(n−1) and P_(n+1) would effectively be given weights of 24 and added to 25 times the value at P_(n), and the values at P_(n−2) and P_(n+2) would effectively be given weights of 22 and also added for the P_(n) pixel position, and so on. The resulting values could then be scaled by dividing by the sum of the seventeen weights shown in FIG. 9.

FIG. 10 shows how the weighting tends to converge to a Gaussian distribution by additional passages through box filters. E.g., a fourth box filter (or even more box filters) could be used if desired.

Returning to FIG. 3, in video effects subsystem 101, original pixel values in frame buffer 110 are fed through five-element box filter 114, one row at a time, with the modified values placed at corresponding positions in frame buffer 112. The values in frame buffer 112 are then fed through five-element box filter 116, one row at a time, with the further modified values placed at corresponding positions in frame buffer 110. The further modified values in frame buffer 110 are then fed through nine-element box filter 118, one row at a time, with the result placed at corresponding positions in frame buffer 112. The resulting values, which are horizontally blurred with near Gaussian distribution, are then similarly passed through the box filters, one column at time, to provide vertically blurring as well. At some point in the processing, the values are divided by the sum of weights at divider 119 (see FIG. 9) to provide scaling.

Referring to FIG. 11, alternative video effects subsystem 140 has a single frame buffer 142 and two five-element box filters 144, 146, a nine-element box filter 148, and a divider 150 all in sequence to achieve the passes through multiple box filters without intermediate storage in frame buffers. The result, which provides horizontal blurring, would need to be stored in frame buffer 110 (after sufficient pipelining to avoid replacing data that still needs to be accessed) so that it could then be accessed in a column-by-column manner to provide vertical blurring.

Other embodiments of the invention are within the scope of the appended claims. For example, box filters having functions other than unity multipliers in each element can be employed, though this will result in a loss of efficiency. Also, one could-use a single box filter, or a series of two, or three (as described above) or more (e.g., four, five, or six, or even more) in series. Also, one can apply a blur to an entire image, as described above, or to a portion of the image, by only accessing the pixel values for the pixels being blurred and some nearby positions. 

1. A computer readable memory medium storing program instructions that are computer executable to: receive two or more original pixel values in a frame of image data, wherein the frame includes original pixel values at pixel positions in a digital image; and for each pixel position in the digital image, calculate a blurred pixel value by applying a first box filter to an original pixel value and nearby pixel values; wherein, for a given original pixel value and its nearby pixel values, the blurred pixel value corresponding to the given original pixel value is calculated without multiplying the nearby pixel values by a weight value.
 2. The computer readable memory medium of claim 1, wherein the first box filter has m elements, and wherein the program instructions are executable to apply the first box filter by: sequentially receiving the two or more original pixel values; maintaining a current filter output value; and updating the current filter output value each time that a new one of the original pixel values is received by adding the new pixel value to the current filter output value and subtracting a pixel value that had been received m pixel positions earlier.
 3. The computer readable memory medium of claim 1, wherein the program instructions are executable to sequentially apply outputs of the first box filter as inputs to a second box filter to compute twice-blurred pixel values.
 4. The computer readable memory medium of claim 3, wherein the first box filter has a different number of elements than the second box filter.
 5. The computer readable memory medium of claim 3, wherein the program instructions are executable to scale each twice-blurred pixel value to compute a corresponding final blurred pixel value.
 6. The computer readable memory medium of claim 1, wherein the two or more received original pixel values include a pixel value P_(n), wherein n corresponds to a position within a given row or given column that includes the two or more received original pixel values, and wherein the program instructions are executable to calculate the blurred pixel value of P_(n) by computing a sum of P_(n) and at least pixel values P_(n−1) and P_(n +1) within the given row or given column that includes P_(n).
 7. The computer readable memory medium of claim 1, wherein the program instructions are executable to achieve, for the two or more original pixel values, a Gaussian or near-Gaussian distribution of nearby pixel values, and wherein, for a given original pixel value of the two or more original pixel values, the nearby pixel values are within the same row or column of the digital image.
 8. A computer readable memory medium storing program instructions that are executable to: perform a blur operation on a first set of original pixel values in a frame of image data, wherein the blur operation includes, for each original pixel value in the first set of pixel values: computing a blurred pixel value for that original pixel value by summing that original pixel value and adjacent pixel values, wherein the blurred pixel value is computed without multiplying the adjacent pixel values by corresponding weight values.
 9. The computer readable memory medium of claim 8, wherein the program instructions executable to perform summing include program instructions executable to implement a first box filter function in which, for a given original pixel value in the first set of original pixel values, the adjacent pixel values are within the same row or column of the frame of image data.
 10. The computer readable memory medium of claim 9, wherein the program instructions executable to perform the blur operation include program instructions executable, for each computed blurred pixel value, to implement a second box filter function to sum that blurred pixel value and its adjacent blurred pixel values to compute a twice-blurred pixel value, wherein the second box filter function does not involve multiplying the adjacent blurred pixel values by corresponding weight values.
 11. The computer readable memory medium of claim 10, wherein the program instructions executable to perform the blur operation include program instructions executable to scale each twice-blurred pixel value to compute a corresponding final blurred pixel value.
 12. The computer readable memory medium of claim 10, wherein the first box filter function has a different number of elements from the second box filter function.
 13. The computer readable memory medium of claim 8, wherein, for a given original pixel value, its blurred pixel value is computed using at least four adjacent pixel values within the row or column of the frame of image data that includes the given original pixel value.
 14. The computer readable memory medium of claim 8, wherein the program instructions are executable to achieve, for each pixel value in the first set of original pixel values, a Gaussian or near-Gaussian distribution of adjacent pixel values.
 15. An apparatus, comprising: a storage unit configured to sequentially receive pixel values from a given row or a given column of a frame buffer; first means for generating blurred pixel values for each of the sequentially received pixel values without performing multiply operations.
 16. The apparatus of claim 15, further comprising: second means for scaling the blurred pixel values generated by the first means.
 17. The apparatus of claim 15, further comprising the frame buffer.
 18. The apparatus of claim 15, wherein the first means includes a plurality of box filter circuits.
 19. The apparatus of claim 18, wherein the box filter circuits do not all have the same number of elements.
 20. The apparatus of claim 15, wherein the first means achieves, for each received pixel value, a Gaussian or near-Gaussian distribution of adjacent pixel values. 